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Showing content from https://keras.io/api/layers/preprocessing_layers/numerical/spectral_normalization below:

SpectralNormalization class

keras.layers.SpectralNormalization(layer, power_iterations=1, **kwargs)

Performs spectral normalization on the weights of a target layer.

This wrapper controls the Lipschitz constant of the weights of a layer by constraining their spectral norm, which can stabilize the training of GANs.

Arguments

layer: A keras.layers.Layer instance that has either a kernel (e.g. Conv2D, Dense...) or an embeddings attribute (Embedding layer).
power_iterations: int, the number of iterations during normalization.
**kwargs: Base wrapper keyword arguments.

Examples

Wrap keras.layers.Conv2D:

>>> x = np.random.rand(1, 10, 10, 1)
>>> conv2d = SpectralNormalization(keras.layers.Conv2D(2, 2))
>>> y = conv2d(x)
>>> y.shape
(1, 9, 9, 2)

>>> x = np.random.rand(1, 10, 10, 1)
>>> dense = SpectralNormalization(keras.layers.Dense(10))
>>> y = dense(x)
>>> y.shape
(1, 10, 10, 10)

Reference

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